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To ensure the stable and secure operation of the power system, we propose a method for short-term load probability prediction based on Conditional Generative Adversarial Network (CGAN) curve generation. Initially, an adaptive integrated prediction model was developed for daily load key values. This model is based on Multi-Attention Bidirectional Long Short-Term Memory (MABi-LSTM) neural networks, utilizing features including date, temperature, and historical load as inputs. Secondly, the maximum information coefficient method is used to weigh the load characteristics, and a similar curve dataset is constructed based on the weighted K-nearest neighbor algorithm and weighted resampling. Then, using load fundamental values and similar curve datasets as conditions and training sets, respectively, a load curve generation model based on CGAN is constructed, and a loss function for correcting numerical deviation and curve shape deviation is proposed. Finally, considering the uncertainty of the model and noise, a mapping relationship from noise to the probability distribution of the model output is constructed for short-term load probability prediction. Taking the load data of a power grid in a particular area of North China, the proposed method has been verified to have higher prediction accuracy than traditional methods.

Power transformers are vital to the power grid and discovering the latent faults in advance is helpful for avoiding serious problems. This study addressed the problem of forecasting and diagnosing the faults of power transformers with small dissolved gas analysis (DGA) data samples that arise from faults in transformers with low occurrence rates. First, an online monitor that was developed in our previous work was applied to obtain the DGA data. Second, the ensemble learning (EL) of a bagging algorithm with bootstrap resampling was used to deal with small training samples. Finally, a criss-cross-optimized neural network (i.e., CSO-NN) was applied to the short-term prediction of the DGA data, based on which the transformer status could be forecasted. The case studies showed that the proposed EL-CSO-NN algorithm integrated into the monitor was capable of achieving satisfactory classification and prediction accuracy for transformer fault forecasting.

The analysis of dissolved gases in insulation oil is of great significance to transformer status evaluation. In this paper, a chromatographic detector based on the nano-tin oxide fiber as well as a chromatography system is developed. The mechanism of the sensor for detecting six component feature gases (i.e., H2, CO, CH4, C2H4, C2H6, and C2H2) in transformer oil is expounded, on basis of which the exponent-logarithmic model between conductance and gas concentrations is proposed. Then, the repeatability and accuracy of the nano-tin oxide detectors are tested. The experimental results show that the gases mixture can be separated well by the designed gas chromatography system, and six component gases mixture detection can be realized by the developed detector. Meanwhile, by using the proposed model, high precision of dissolved gas measurement can be achieved, thus the validity of the presented model is verified. Moreover, compared with other chromatographic detectors, i.e., flame ionization detector, the only carrier gas needed for the nano-tin oxide chromatographic detector is the synthetic air, the hardware cost and complexity of system are reduced largely, showing promising applicable value in the engineering practice.

This paper is concerned with the airborne-laser-data-based sag estimation for wide-area transmission lines. A systematic data processing framework is established for multi-source data collected from power lines, which is applicable to various operating conditions. Subsequently, a k-means-based clustering approach is employed to handle the spatial heterogeneity and sparsity of powerline corridor data after comprehensive performance comparisons. Furthermore, a hybrid model of the catenary and XGBoost (HMCX) method is proposed for sag estimation, which improves the accuracy of sag estimation by integrating the adaptability of catenary and the sparsity awareness of XGBoost. Finally, the effectiveness of HMCX is verified by using power data from 116 actual lines.

With the rapid advance of e-learning, the online peer help is playing increasingly important role. This paper explores the application of MAS to an online peer help system (MAPS). In the design phase, the architecture of MAPS is proposed, which consists of a set of agents including the personal agent, the course agent, the diagnosis agent, the DF agent and the communication agent. Through cooperation and negotiation, these agents are able to provide just-in-time and personalized peer help service on demand. In particular, it makes easy to perform such tasks as registering, searching and subscribing help services in a flexible fashion in MAPS. To clearly dem-onstrate the interactivity and message passing among agents, an UML sequence diagram is used to illustrate the peer help process. On this basis, the agent modeling approach is also discussed. In the phase of implementation, the JADE based agent behavior model was described in detail and the imple-mentation platform was also proposed. In the end, a demon-stration is used to exhibit the feasibility and reasonability of the proposed model and approach. [ABSTRACT FROM AUTHOR]